PhD: Biological Sciences

University of Missouri, Columbia, MO (2021)

Machine Learning Methods for Biomedical Informatics, Quantitative Methods in the Life Sciences, and Grant Writing

Bachelor of Science: Biology

Truman State University, Kirksville, MO (2015)

Next Generation Sequence Data and Analysis, Bioinformatics, ANOVA and Experimental Design, Non-Parametric Statistics

Leadership role in the biological honors society Tri-Beta

Technical Skills

: R Programming (6 years) experience with tidyverse, lme4, caret, ggplot2, shiny, & package creation.

: Python Programming (2 years) experience with pandas, numpy, plotly, scikit-learn, keras, pytorch.

: Miscellaneous Experience with high performance computing (bash, slurm), virtual environments (conda, singularity, docker), version control (git, GitHub), literate programming (Rmarkdown, Jupyter).

Research Geneticist

Jacob Washburn Lab, USDA-ARS

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Present - 2021

• Employed deep neural networks, machine learning models, and best linear unbiased predictors to improve corn yield prediction accuracy in diverse environments (see Kick et al., 2023).
• Designed project plan and submitted grant application titled “Environmentally Aware Deep Learning Based Genomic Selection And Management Optimization For Maize Yield” to the NIFA Agriculture and Food Research Initiative’s Education and Workforce Development Program (Decision Pending).
• Communicated with stakeholders through 8 presentations (4 national, 2 regional, and 2 outreach presentations).
• Mentored 2 students conducting a high throughput root phenotyping experiment and wrote scripts for data organization and analysis.
• Created and taught a Python data visualization workshop titled “Tools and Techniques for a Jupyter Based Scientific Workflow”.
• Collaborated with plant biologists, contributing statistical expertise (manuscript in preparation).
• Completed Software Carpentries instructor certification, taught R for Reproducible Scientific Analysis, and assisted in teaching Data Management with SQL.
• Designed and completed a professional development curriculum with the guidance of an industry scientist via the Bayer-University Mentoring Program.
• Served as a panel member on “Next-Generation Omics” at the 2022 University of Missouri Division of Biological Sciences Retreat.

Graduate Researcher

David Schulz Lab, University of Missouri

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2021 - 2016

• Author on 6 publications: 4 original research and 2 eLife Insight publications.
• Assessed the efficacy of machine learning models to identify cell identity from mRNA and contig abundances. Applied cluster estimation, hyperparameter tuning, unsupervised machine learning, and supervised machine learning. Identified and learned needed skills primarily through self study. Collaborated with molecular biology project lead. (see Northcutt¹, Kick¹, et al. 2019).
• Demonstrated activity desynchronization induces changes in neuronal connections. Defined research question and experiments. Developed novel approach to quantify changes in cell activity (see Kick and Schulz 2022).
• Investigated activity dependent changes in neuronal excitability, conductances, and ion channel mRNA abundances. Designed experiments, collected data, performed analysis, and developed novel method for quantifying changes in cell activity using in silico simulations.
• Collaborated with electrophysiologists, assisting with data organization, cleaning, and analysis.
• Collaborated with computational neuroscientists, contributing biological and statistical expertise (in preparation).
• Mentored 5 students and oversaw their projects.
• Communicated results through 17 presentations (6 national, 6 regional, and 5 outreach and recruitment).
• Served as a peer mentor of 3 PhD students in use of R for reproducible data analysis, created internal documents on same.

Lead Teaching Assistant, Animal Physiology Lab

Biological Sciences, University of Missouri

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2021 - 2020

• Developed statistics web application used by more than 700 students as of 2021 with shiny (source, deployed) for data visualization, testing assumptions, and fitting frequentist, non-parametric, and Bayesian models.
• Led 4 Teaching Assistants and coordinated adaptation of lab curriculum to be fully online due to COVID-19 pandemic.
• Mentored next Lead Teaching Assistant, created documentation on best practices.

Teaching Assistant, Animal Physiology Lab

Biological Sciences, University of Missouri

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2020 - 2018

• Updated and refined curriculum.
• Delivered lectures and ensured experiments were conducted safely.
• Modeled student grade distributions to identify and adjust for differences in grading.

Curriculum Consultant, Animal Physiology Lab

Biological Sciences, University of Missouri

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2018

• Redesigned course material to incorporate primary literature and data analysis.

Teaching Assistant, Animal Physiology Lab

Biological Sciences, University of Missouri

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2016 - 2015

• Delivered weekly lectures, ensured experiments were conducted safely, provided timely feedback on assignments.

Undergradute Researcher

University of Missouri, University of Connecticut, and Truman State University

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2013 - 2015

• Designed a hydroponic system for maize root phenotyping – Diane Janick-Buckner and Brent Buckner, Truman State University (2014-2015), Quantified retinal minor splicisome expression using immunohistochemistry – (NSF REU) Rahul Kanadia, University of Connecticut (2014), Measured effectiveness of oligonucleotide treatment for spinal muscular atrophy in mice – (NSF REU) Christian Lorson, University of Missouri (2013).

Ranked First in University of Missouri Plant Research Symposium Poster Competition

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2022

J. Perry Gustafson Award for Outstanding Graduate Research in the Life Sciences

This award is granted for the quality of a student’s independent research and academic achievements. Recipients receive a $2,000 award.

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2019

National Institutes of Health T32 Training Grant Recipient

This fellowship provides a $27,000 yearly stipend and travel awards of $750.

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2018 - 2016

Cum Laude & President’s Recognition, Truman State University

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2015

Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning

Daniel R. Kick, Jason G. Wallace, James C. Schnable, Judith M. Kolkman, Baris Alaca, Timothy M. Beissinger, David Ertl, Sherry Flint-Garcia, Joseph L. Gage, Candice N. Hirsch, Joseph E. Knoll, Natalia de Leon, Dayane C. Lima, Danilo Moreta, Maninder P. Singh, Teclemariam Weldekidan, Jacob D. Washburn G3: Genes, Genomes, Genetics

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2023

Timing dependent potentiation and depression of electrical synapses contributes to network stability in the crustacean cardiac ganglion

Daniel R. Kick and David J. Schulz The Journal of Neuroscience

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2022

Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis

Adam J. Northcutt¹, Daniel R. Kick¹, Adriane G. Otopalik, Benjamin M. Goetz, Rayna M. Harris, Joseph M. Santin, Hans A. Hofmann, Eve Marder, and David J. Schulz (¹ denotes co-first authorship) Proceedings of the National Academy of Sciences

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2019